E-Books →Artificial Intelligence for Excel Architecture, Models, and Implementation

Free Download Artificial Intelligence for Excel: Architecture, Models, and Implementation
English | November 23, 2025 | ASIN: B0G3KTCZYZ | 268 pages | Epub | 3.10 MB
"Artificial Intelligence for Excel: Architecture, Models, and Implementation" is a comprehensive textbook designed to revolutionize the way Computer Science students and professionals approach Data Science. In an era where Artificial Intelligence (AI) is redefining industries, this book serves as a crucial bridge, connecting the theoretical rigor of academic computer science with the practical, ubiquitous utility of Microsoft Excel. Why This Book? While many books teach AI using complex IDEs like PyCharm or Jupyter Notebooks, they often alienate beginners. Conversely, Excel books rarely touch upon deep tech. This book sits in the "Goldilocks Zone"-technically robust enough for a Master's degree curriculum, yet accessible enough for a beginner. It empowers the reader to say, "I can build an AI model today," using the tools they already have. Key Features: This book is distinguished by the following features, designed to cater to B.Tech/M.Tech students and global learners: Curriculum 1. Compatibility: The content maps directly to standard university syllabi for "Artificial Intelligence," "Machine Learning," and "Data Analytics," making it an ideal course textbook. 2. Architecture-First Approach: Unlike standard Excel books, this text explains the System Design. It covers API integration, Cloud Computing (Azure/AWS), and the internal architecture of how Excel processes Python scripts. 3. Python in Excel Integration: Dedicated chapters cover the latest technological breakthrough-running Python natively within Excel cells-allowing users to leverage libraries like pandas, matDescriptionlib, and scikit-learn without complex setups. 4. No-Code & Low-Code AI: For beginners, the book demonstrates Excel's built-in AI features (Analyze Data, Forecast Sheets) and add-ins that require zero coding. 5. Visual Learning: The book is packed with architectural diagrams, flowcharts, and to explain the "Black Box" of AI functioning. 6. Capstone Project: A full DIY project in the final chapter ensures the learner can build a deployable product from scratch. Target Audience: 1. B.Tech / M.Tech Computer Science Students: For understanding the practical application of ML algorithms and system architecture. 2. Data Analysts & Business Intelligence Professionals: For upgrading skills from basic reporting to predictive modeling. 3. Research Scholars: For utilizing Excel as a rapid prototyping tool for data models. 4. MBA / Management Students: To understand the business applications of AI without needing deep coding expertise.